






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)


@require_tf
class  snake_case__      :
					a_        =					XGLMConfig
					a_        =					{}
					a_        =					"gelu"




					def __init__(  self  :		Optional[int]				, _A  :		Dict				, _A  :		str=14				, _A  :		Tuple=7				, _A  :		Optional[Any]=True				, _A  :		List[Any]=True				, _A  :		List[str]=True				, _A  :		List[Any]=99				, _A  :		Dict=32				, _A  :		Optional[int]=2				, _A  :		Dict=4				, _A  :		int=37				, _A  :		Any="gelu"				, _A  :		int=0.1				, _A  :		Dict=0.1				, _A  :		Optional[int]=5_12				, _A  :		Union[str, Any]=0.02				, )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       parent
								UpperCAmelCase_      :       Dict          =       batch_size
								UpperCAmelCase_      :       int          =       seq_length
								UpperCAmelCase_      :       List[Any]          =       is_training
								UpperCAmelCase_      :       Tuple          =       use_input_mask
								UpperCAmelCase_      :       List[Any]          =       use_labels
								UpperCAmelCase_      :       Optional[int]          =       vocab_size
								UpperCAmelCase_      :       Optional[int]          =       d_model
								UpperCAmelCase_      :       List[str]          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       num_attention_heads
								UpperCAmelCase_      :       Any          =       ffn_dim
								UpperCAmelCase_      :       Optional[int]          =       activation_function
								UpperCAmelCase_      :       Optional[int]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       attention_dropout
								UpperCAmelCase_      :       List[Any]          =       max_position_embeddings
								UpperCAmelCase_      :       str          =       initializer_range
								UpperCAmelCase_      :       Any          =       None
								UpperCAmelCase_      :       Any          =       0
								UpperCAmelCase_      :       Optional[int]          =       2
								UpperCAmelCase_      :       int          =       1




					def 		A      (  self  :		Optional[Any]      )    ->							int:
								return XGLMConfig.from_pretrained('''facebook/xglm-564M'''      )




					def 		A      (  self  :		Dict      )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       tf.clip_by_value(
								    ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size      )				, clip_value_min=0				, clip_value_max=3      )

								UpperCAmelCase_      :       Tuple          =       None
								if self.use_input_mask:
											UpperCAmelCase_      :       Optional[Any]          =       random_attention_mask([self.batch_size, self.seq_length]      )

								UpperCAmelCase_      :       Optional[Any]          =       self.get_config()

								UpperCAmelCase_      :       int          =       floats_tensor([self.num_hidden_layers, self.num_attention_heads]				, 2      )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								)




					def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
								return XGLMConfig(
								    vocab_size=self.vocab_size				, d_model=self.hidden_size				, num_layers=self.num_hidden_layers				, attention_heads=self.num_attention_heads				, ffn_dim=self.ffn_dim				, activation_function=self.activation_function				, activation_dropout=self.activation_dropout				, attention_dropout=self.attention_dropout				, max_position_embeddings=self.max_position_embeddings				, initializer_range=self.initializer_range				, use_cache=_A				, bos_token_id=self.bos_token_id				, eos_token_id=self.eos_token_id				, pad_token_id=self.pad_token_id				, return_dict=_A				, )





					def 		A      (  self  :		str      )    ->							int:
								UpperCAmelCase_      :       Optional[Any]          =       self.prepare_config_and_inputs()

								(
								    (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      
								)      :       Optional[int]          =       config_and_inputs

								UpperCAmelCase_      :       List[Any]          =       {
								    '''input_ids''': input_ids,
								    '''head_mask''': head_mask,
								}

								return config, inputs_dict






@require_tf
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					a_        =					(TFXGLMForCausalLM,) if is_tf_available() else ()
					a_        =					(
					    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
					)
					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       TFXGLMModelTester(self      )
								UpperCAmelCase_      :       str          =       ConfigTester(self				, config_class=_A				, n_embd=37      )




					def 		A      (  self  :		Dict      )    ->							List[Any]:
								self.config_tester.run_common_tests()




					@slow
					def 		A      (  self  :		Optional[int]      )    ->							Any:
								for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase_      :       List[Any]          =       TFXGLMModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''      )
					def 		A      (  self  :		int      )    ->							List[str]:
								super().test_resize_token_embeddings()






@require_tf
class  snake_case__      (					unittest.TestCase):




					@slow
					def 		A      (  self  :		List[str]				, _A  :		Dict=True      )    ->							Any:
								UpperCAmelCase_      :       str          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )
								UpperCAmelCase_      :       Any          =       tf.convert_to_tensor([[2, 2_68, 98_65]]				, dtype=tf.intaa      )  # The dog
								# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
								# fmt: off
								UpperCAmelCase_      :       Optional[Any]          =       [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
								# fmt: on
								UpperCAmelCase_      :       Tuple          =       model.generate(_A				, do_sample=_A				, num_beams=1      )
								if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()				, _A      )




					@slow
					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''      )
								UpperCAmelCase_      :       Optional[int]          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )

								tf.random.set_seed(0      )
								UpperCAmelCase_      :       Tuple          =       tokenizer('''Today is a nice day and'''				, return_tensors='''tf'''      )
								UpperCAmelCase_      :       Optional[Any]          =       tokenized.input_ids
								# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
								with tf.device(''':/CPU:0'''      ):
											UpperCAmelCase_      :       Optional[int]          =       model.generate(_A				, do_sample=_A				, seed=[7, 0]      )
								UpperCAmelCase_      :       List[str]          =       tokenizer.decode(output_ids[0]				, skip_special_tokens=_A      )

								UpperCAmelCase_      :       Dict          =       (
								    '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
								)
								self.assertEqual(_A				, _A      )





					@slow
					def 		A      (  self  :		List[str]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )
								UpperCAmelCase_      :       Optional[int]          =       XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''      )

								UpperCAmelCase_      :       str          =       '''left'''

								# use different length sentences to test batching
								UpperCAmelCase_      :       List[str]          =       [
								    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
								    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
								    '''regardless of whether left padding is applied or not. When''',
								    '''Hello, my dog is a little''',
								]

								UpperCAmelCase_      :       Optional[int]          =       tokenizer(_A				, return_tensors='''tf'''				, padding=_A      )
								UpperCAmelCase_      :       List[Any]          =       inputs['''input_ids''']

								UpperCAmelCase_      :       List[str]          =       model.generate(input_ids=_A				, attention_mask=inputs['''attention_mask''']				, max_new_tokens=12      )

								UpperCAmelCase_      :       str          =       tokenizer(sentences[0]				, return_tensors='''tf'''      ).input_ids
								UpperCAmelCase_      :       Dict          =       model.generate(input_ids=_A				, max_new_tokens=12      )

								UpperCAmelCase_      :       Any          =       tokenizer(sentences[1]				, return_tensors='''tf'''      ).input_ids
								UpperCAmelCase_      :       Union[str, Any]          =       model.generate(input_ids=_A				, max_new_tokens=12      )

								UpperCAmelCase_      :       Optional[Any]          =       tokenizer.batch_decode(_A				, skip_special_tokens=_A      )
								UpperCAmelCase_      :       Optional[int]          =       tokenizer.decode(output_non_padded[0]				, skip_special_tokens=_A      )
								UpperCAmelCase_      :       Dict          =       tokenizer.decode(output_padded[0]				, skip_special_tokens=_A      )

								UpperCAmelCase_      :       List[str]          =       [
								    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
								    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
								    '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
								    '''a single''',
								    '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
								]
								self.assertListEqual(_A				, _A      )
								self.assertListEqual(_A				, [non_padded_sentence, padded_sentence]      )










'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Dict=2				, _A  :		Optional[Any]=32				, _A  :		List[str]=16				, _A  :		str=3				, _A  :		List[Any]=True				, _A  :		Optional[int]=True				, _A  :		Optional[int]=32				, _A  :		Optional[Any]=4				, _A  :		Optional[int]=[0, 1, 2, 3]				, _A  :		List[Any]=4				, _A  :		Optional[int]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Optional[Any]=0.1				, _A  :		Union[str, Any]=0.1				, _A  :		Dict=0.02				, _A  :		Optional[Any]=3				, _A  :		Union[str, Any]=[1, 3_84, 24, 24]				, _A  :		int=True				, _A  :		int=None				, )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       image_size
								UpperCAmelCase_      :       List[Any]          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       str          =       is_training
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       Optional[Any]          =       hidden_size
								UpperCAmelCase_      :       str          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       backbone_out_indices
								UpperCAmelCase_      :       str          =       num_attention_heads
								UpperCAmelCase_      :       List[Any]          =       intermediate_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_act
								UpperCAmelCase_      :       Dict          =       hidden_dropout_prob
								UpperCAmelCase_      :       Union[str, Any]          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       List[str]          =       initializer_range
								UpperCAmelCase_      :       int          =       num_labels
								UpperCAmelCase_      :       Union[str, Any]          =       backbone_featmap_shape
								UpperCAmelCase_      :       List[str]          =       scope
								UpperCAmelCase_      :       List[str]          =       is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_      :       List[Any]          =       (image_size // patch_size) ** 2
								UpperCAmelCase_      :       List[str]          =       num_patches + 1




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]				, self.num_labels      )

								UpperCAmelCase_      :       Dict          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
								    '''num_groups''': 2,
								}

								return DPTConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_A				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, backbone_config=_A				, backbone_featmap_shape=self.backbone_featmap_shape				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Optional[int]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[int]          =       DPTModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[str]          =       model(_A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )




					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.num_labels
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(_A      )
								self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)      )




					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Union[str, Any]				, _A  :		int      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       Optional[Any]          =       DPTForSemanticSegmentation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Tuple          =       model(_A				, labels=_A      )
								self.parent.assertEqual(
								    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





					def 		A      (  self  :		Dict      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Tuple          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					a_        =					(
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Any          =       DPTModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, has_text_modality=_A				, hidden_size=37      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								self.config_tester.run_common_tests()




					@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								pass




					def 		A      (  self  :		str      )    ->							Dict:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Union[str, Any]          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Dict          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Dict          =       model_class(_A      )
											UpperCAmelCase_      :       Optional[Any]          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       int          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Any          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_A      )




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       Union[str, Any]          =       True

											if model_class in get_values(_A      ):
														continue

											UpperCAmelCase_      :       Optional[int]          =       model_class(_A      )
											model.to(_A      )
											model.train()
											UpperCAmelCase_      :       Any          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       List[Any]          =       False
											UpperCAmelCase_      :       Optional[Any]          =       True

											if model_class in get_values(_A      ) or not model_class.supports_gradient_checkpointing:
														continue
											UpperCAmelCase_      :       int          =       model_class(_A      )
											model.to(_A      )
											model.gradient_checkpointing_enable()
											model.train()
											UpperCAmelCase_      :       int          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       List[str]          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       List[str]          =       _config_zero_init(_A      )
								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       model_class(config=_A      )
											# Skip the check for the backbone
											UpperCAmelCase_      :       List[str]          =       []
											for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	UpperCAmelCase_      :       Union[str, Any]          =       [F"{name}.{key}" for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F"Parameter {name} of model {model_class} seems not properly initialized"				, )




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Tuple      )    ->							Optional[Any]:
								pass




					@slow
					def 		A      (  self  :		Any      )    ->							int:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											UpperCAmelCase_      :       Any          =       DPTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Optional[int]          =       '''add'''
								with self.assertRaises(_A      ):
											UpperCAmelCase_      :       Optional[Any]          =       DPTForDepthEstimation(_A      )






def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[Any]          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image







@require_torch
@require_vision
@slow
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       List[Any]          =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(_A      )

								UpperCAmelCase_      :       Tuple          =       prepare_img()
								UpperCAmelCase_      :       Union[str, Any]          =       image_processor(images=_A				, return_tensors='''pt'''      ).to(_A      )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_      :       str          =       model(**_A      )
											UpperCAmelCase_      :       List[Any]          =       outputs.predicted_depth

								# verify the predicted depth
								UpperCAmelCase_      :       str          =       torch.Size((1, 3_84, 3_84)      )
								self.assertEqual(predicted_depth.shape				, _A      )

								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(_A      )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				, _A				, atol=1e-4      )      )


